Ultra-compact sprite rendering code with example frame animation logic. This release contains tech bits from the upcoming SuperNeo™ 2D game engine and includes anchor/pivot point, rotation, color filtering, alpha blending and built-in antialiased point sampling. As usual: complete, runnable single-function app. ~150 LOC. No modern C++, OOP or (other) obscuring cruft.

Sprites are rendered back-to-front (AKA "painter's algorithm") in the order they are submitted, as one draw call. The provided setup employs a single texture atlas containing all the sprite graphics.

The renderer is "im

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

by Flamerix AI · MIT License

Tested on Linux, Claude Code 2.1.257–2.1.312 (see Tested versions). The binary is not modified (BUN_OPTIONS=--preload). An update can silently undo it, so see Verify.

curl -fsSL https://gist.githubusercontent.com/flamerix-ai/c3905705cc4205e5de3011fa2e74efce/raw/no-agent-summary.js -o ~/.local/share/no-agent-summary.js

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.